B1408
Title: High-dimensional knockoffs inference for time series data
Authors: Chien-Ming Chi - Academia Sinica (Taiwan) [presenting]
Abstract: The model-X knockoffs framework provides a flexible tool for achieving finite-sample false discovery rate (FDR) control in variable selection in arbitrary dimensions without assuming any dependence structure of the response on covariates. It also completely bypasses conventional p-values, making it especially appealing in high-dimensional nonlinear models. Existing works have focused on the setting of independent and identically distributed observations. Yet, time series data is prevalent in practical applications in various fields such as economics and social sciences. This motivates the study of model-X knockoffs inference for time series data. Some initial attempts are made to establish the theoretical and methodological foundation for the model-X knockoffs inference for time series data. The method of time series knockoffs inference (TSKI) is suggested by exploiting the ideas of subsampling and e-values to address the difficulty caused by the serial dependence. The robust knockoffs inference is also generalized in another study to the time series setting and relax the assumption of known covariate distribution required by model-X knockoffs, because such an assumption is overly stringent for time series data. Sufficient conditions are established under which TSKI achieves the asymptotic FDR control. The technical analysis reveals the effects of serial dependence and unknown covariate distribution on the FDR control.
